用机器学习的自我报告问卷预测社区老年人的自杀念头
Kyungwon Kim1,2,3, Eunsoo Moon1,2,3,4, Hyunju Lim1,2
1Department of Psychiatry, Pusan National University Hospital, Busan, Korea.
概括
在老年人中预测自杀念头是可能的,使用自我报告尺度. 机器学习模型,特别是那些使用患者健康问卷-9 (PHQ-9) 的更多项目的机器学习模型,在识别面临自杀预防风险的个体方面表现出很高的准确性.
科学领域:
- 老年学是指老年学的学科.
- 公共卫生 公共卫生
- 计算精神病学是一种计算精神病学.
背景情况:
- 自杀是一个关键的公共卫生问题,老年人面临着更高的风险.
- 早期识别自杀念头对于有效的干预和预防策略至关重要.
研究的目的:
- 开发一种机器学习模型,用于预测社区老年人的自杀念头.
- 评估精神病学自我报告尺度在识别有风险的老年人的有效性.
主要方法:
- 通过使用PHQ-9,GAD-7,PSS-10和WHOQOL-BREF评估了238名老年人.
- 采用嵌套的5倍交叉验证,进行100次重复,用于特征选择和模型评估.
- 利用各种机器学习分类器,包括SVM,随机森林和渐变增强.
主要成果:
- 用AUC来衡量模型性能,从0.835增加到0.892,因为PHQ-9项目从2增加到6.
- 使用九个稳定特征将AUC提高到0.904,证明了信息项目的好处.
- 这项研究证实了精神病学自我报告尺度对自杀念头的预测能力.
结论:
- 精神病学自我报告尺度有效地预测了老年人的自杀想法风险.
- 优化特征选择可以提高早期识别系统的预测模型准确性.
- 研究结果支持基于社区的自杀预防计划,包括对高危老年人的查.
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